bridging biological ontologies and biosimulation: the ontology of physics for biology daniel l. cook...
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Bridging Biological Ontologies and Biosimulation:
The Ontology of Physics for Biology
Daniel L. Cook 1, 2
John H. Gennari 3
Jose L. V. Mejino 2
Maxwell L. Neal 3
1Physiology & Biophysics, 2Biological Structure3Biomedical and Health InformaticsUniversity of Washington, Seattle
AMIA 2008, Washington, DC
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
Available bioinformatics for “multiscale” structure
extended from Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.
Foundational Model of Anatomy
Gene Ontology
ChEBI
Cell Type
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
No bioinformatics for multidomain processes
fluids
solids
diffusion
heat transfer
myocardial contraction, leg motion…
electrochemistry transmembrane potential, action potential…
chemical kinetics metabolism, gene expression, cell signaling…
body temperature regulation…
intracellular calcium dynamics…
blood flow, respiratory gas flow…
Physical Physical domainsdomains
Domain Process
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
Bioinformatic problem: query process knowledge
fluids
solids
diffusion
heat transfer
myocardial contraction, leg motion…
electrochemistry transmembrane potential, action potential…
chemical kinetics metabolism, gene expression, cell signaling…
body temperature regulation…
intracellular calcium dynamics…
blood flow, respiratory gas flow…
Physical Physical domainsdomains
Domain Process
• How is blood pressure controlled?• Which nerves control blood
pressure?
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
Processes encoded as biosimulations models
fluids
solids
diffusion
heat transfer
myocardial contraction, leg motion…
electrochemistry transmembrane potential, action potential…
chemical kinetics metabolism, gene expression, cell signaling…
body temperature regulation…
intracellular calcium dynamics…
blood flow, respiratory gas flow…
Physical Physical domainsdomains
Domain Process
physics-basedbiosimulation model
physics-basedbiosimulation model
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
Available models constitute “physiome”
fluids
solids
diffusion
heat transfer
myocardial contraction, leg motion…
electrochemistry transmembrane potential, action potential…
chemical kinetics metabolism, gene expression, cell signaling…
body temperature regulation…
intracellular calcium dynamics…
blood flow, respiratory gas flow…
Physical Physical domainsdomains
Domain Process
PhysiomePhysiome
Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.
63 organ types
2 bodies
>> 100,000 molecule types
>400 cell-part types
12 organ systems
> 100 elements
>600 cell types
Physiome problem: reuse and merge models
fluids
solids
diffusion
heat transfer
myocardial contraction, leg motion…
electrochemistry transmembrane potential, action potential…
chemical kinetics metabolism, gene expression, cell signaling…
body temperature regulation…
intracellular calcium dynamics…
blood flow, respiratory gas flow…
Physical Physical domainsdomains
Domain Process
PhysiomePhysiome
Hunter, P. J. & Borg, T. K. (2003). Nat Rev Mol Cell Biol 24(6):667-72.
physics-basedbiosimulation model
physics-basedbiosimulation model
Proposal for a solution:
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Biosimulation
model code
Biosimulation
model codeSemSimSemSimReference
ontologiesReferenceontologies
Outline:
Biosimulation
model code
Biosimulation
model codeSemSimSemSimOPB, FMA,
GO, CheBI, etc.OPB, FMA,
GO, CheBI, etc.
• Problems: biosimulation, bioinformatics
• SemSim ontology• Ontology of Physics for Biology
(OPB)• Conclusion
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
structuralknowledge
physicsknowledge
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
fluidssolids
chemical kinelectrochem
diffusionheat transfer
Time
In practice: code is hand-crafted
Biophysicists and bioengineers encode physics-based
mathematical models of biological processes
In practice: code is formal — meaning is implicit
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
real Paorta(t) mmHg; // Pressure of aortareal PSysVein(t) mmHg; // Pressure of systemic veinreal FSysArt(t) ml/sec; // Flow in systemic artery
real Rartcap = 0.7 mmHg*sec/ml; // Arterial resistance
FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's
Law
real Paorta(t) mmHg; // Pressure of aortareal PSysVein(t) mmHg; // Pressure of systemic veinreal FSysArt(t) ml/sec; // Flow in systemic artery
real Rartcap = 0.7 mmHg*sec/ml; // Arterial resistance
FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's
Law
physiological variable
names are arbitrary
anatomical participants
known only by annotation
variable dependencies known only by
annotation
In practice: multiple, incompatible languages
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
JSim, SBML, CellML, MatLab,
others…
JSim, SBML, CellML, MatLab,
others…
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
In practice: 100’s of models in linguistic silos
structuralknowledge CellML
SBML
JSim
MatLab
other
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
Opportunity: a reservoir of process knowledge
CellML
SBML
JSim
MatLab
other
Problem: barriers to biosimulation model reuse
CellML
SBML
JSim
MatLab
physics-basedprocess
biosimulation
physics-basedprocess
biosimulation
other
JSim JSim
?
?
?
How to find, merge and re-encode models?
Problem: no access for bioinformatic queries
CellML
SBML
JSim
MatLab
other
Q & A
How to query knowledge of biological processes? SparQL SparQL
Two fields, two problems:
• Find models of blood pressure control.
• Which models include neural-control?
Biosimulation — re-use biosimulation models
• How is blood pressure controlled?• Which nerves control blood pressure?
Bioinformatics — query process knowledge
Outline:
Biosimulation
model code
Biosimulation
model codeSemSimSemSimOPB, FMA,
GO, CheBI, etc.OPB, FMA,
GO, CheBI, etc.
• Problems: biosimulation, bioinformatics
• SemSim ontology• Ontology of Physics for Biology
(OPB)• Conclusion
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Solution: encode SemSim ontological maps…
CellML
SBML
JSim
MatLab
other
SemSimSemSimSemSimSemSim
SemSimSemSimSemSimSemSim
SemSimSemSimOWL
SemSimsemantic maps of
biosimulation models
OPB, FMA, GO, CheBI, etc.
OPB, FMA, GO, CheBI, etc.
…and annotate to reference ontologies
CellML
SBML
JSim
MatLab
other
SemSimSemSimSemSimSemSim
SemSimSemSimSemSimSemSim
SemSimSemSimOWL
annotate SemSim components to
reference ontologies
SemSimsemantic maps of
biosimulation models
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
SemSim — biosimulation ontological map
SemSim modelSemSim model
biosimulation code
Computational model
Physicalmodel
Gennari, J. H., M. L. Neal, B. E, Carlson, D. L. Cook (2008) Integration of multi-scale biosimulation models via light-weight semanticsPac Symp Biocomput (414-425)
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
SemSim — step 1: represent math structure
SemSim modelSemSim modelComputational
model
Data structure
biosimulation code
Physicalmodel
represent variable as individuals of class
Data structure
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
SemSim — step 1: represent math structure
SemSim modelSemSim modelComputational
model
Computation
Data structure
use / return
biosimulation code
Physicalmodel
represent variable as individuals of class
Data structure
represent equations as individuals of class
Computation
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
SemSim — step 2: represent biological meaning
SemSim modelSemSim model
Physicalproperty
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation code
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
e.g., volume, pressure, molar flow, chemical
amount
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
SemSim — step 2: represent biological meaning
SemSim modelSemSim model
Physicalentity
has_property
Physicalproperty
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation code
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
e.g., heart, blood in aorta, protein
kinase, folate, Ca++
e.g., volume, pressure, molar flow, chemical
amount
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
SemSim — step 2: represent biological meaning
SemSim modelSemSim model
Physicalentity
has_property
Physical dependency
Physicalproperty
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation code
has_player
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
e.g., heart, blood in aorta, protein
kinase, folate, Ca++
e.g., volume, pressure, molar flow, chemical
amount
e.g., Ohm’s law, law of mass action, mass conservation
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
SemSim modelSemSim model
Physicalentity
has_property
Physical dependency
Physicalproperty
has_player
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation codeGO
ChEBI
FMA
Map to reference ontologies of structure
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
SemSim modelSemSim model
Physicalentity
has_property
Physical dependency
Physicalproperty
has_player
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation codeGO
ChEBI
FMA
OPB
Map to reference ontology of physics — OPB
Outline:
Biosimulation
model code
Biosimulation
model codeSemSimSemSimOPB, FMA,
GO, CheBI, etc.OPB, FMA,
GO, CheBI, etc.
• Problems: biosimulation, bioinformatics
• SemSim ontology• Ontology of Physics for Biology
(OPB)• Conclusion
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
Semantics of biosimulation models can be encoded as ontologies and
mapped to reference ontologies.
OPB foundational theory — system dynamics
Engineering system dynamics• Bond graph theory
Karnopp, Margolis, Rosenberg (1968)
• EngMath - Ontology for Engineering Mathematics
Gruber, Olsen (1994)
• PHYSYS - Physical Systems OntologyBorst, Top, Akkermans (1994)
Biochemical system dynamics• Network thermodynamics
Oster, Perelson, Katchalsky (1971)Mickulecky (1983)Beard, Qian (2008)
Engineering system dynamics• Bond graph theory
Karnopp, Margolis, Rosenberg (1968)
• EngMath - Ontology for Engineering Mathematics
Gruber, Olsen (1994)
• PHYSYS - Physical Systems OntologyBorst, Top, Akkermans (1994)
Biochemical system dynamics• Network thermodynamics
Oster, Perelson, Katchalsky (1971)Mickulecky (1983)Beard, Qian (2008)
OPB representational goals
• Represent abstractions used in physics-based biosimulations—not a theory of “reality”.
• Adhere to OBO principles.
• Implement in OWL; deploy to OBO and BioPortal.
• Represent abstractions used in physics-based biosimulations—not a theory of “reality”.
• Adhere to OBO principles.
• Implement in OWL; deploy to OBO and BioPortal.
OPB:Physics analytical entity
OPB
A Physics analytical entity is an abstraction of the real world created
within the science of classical physics for the description of physical entities
and the analysis of physical processes.
OPB:Physical entity
OPB
A Physics analytical entity is an abstraction of the real world created
within the science of classical physics for the description of physical entities
and the analysis of physical processes.
A Physical entity is a spatial, temporal, or energetic abstraction
of the physical world.
OPB:Physical property
OPB
A Physics analytical entity is an abstraction of the real world created
within the science of classical physics for the description of physical entities
and the analysis of physical processes.
A Physical property is a quantifiable attribute of a physical entity whose
value can be determined by physical measurement at a moment in time.
A Physical entity is a spatial, temporal, or energetic abstraction
of the physical world.
chemical kinetics
volume flow pressure volume pressure momentum
velocity force displacement solid momentum
molar flow chemical potential chemical amount ----
ionic current voltage charge ----
particle flow chemical potential particle number ----
heat flow temperature heat amount ----
fluids
solids
electrophysiology
diffusion
heat transfer
Physical property organizing principle
Physical domain
chemical kinetics
volume flow pressure volume pressure momentum
velocity force displacement solid momentum
molar flow chemical potential chemical amount ----
ionic current voltage charge ----
particle flow chemical potential particle number ----
heat flow temperature heat amount ----
fluids
solids
electrophysiology
diffusion
heat transfer
Physical property class hierarchy
Physical domain
OPB
A Flow subclass for each physical
domain
Physical property by domain
OPB
A Physics analytical entity is an abstraction of the real world created
within the science of classical physics for the description of physical entities
and the analysis of physical processes.
A Physical property is a quantifiable attribute of a physical entity whose
value can be determined by physical measurement at a moment in time.
A Physical entity is a spatial, temporal, or energetic abstraction
of the physical world.
A Physical dependency is a quantitative dependency between the magnitudes of two or more physical
properties according to a physical law.
Physical dependency
Physical dependency organizing principle
A Physical dependency is a quantitative dependency between the magnitudes of two or more physical
properties according to a physical law.
Axiomatic physical dependency
Constitutive physical dependency
Flo
w
e.g., “Ohm’s law”
Force
Constitutive physical dependency
Flo
w
Force
Dis
pla
cem
en
t
Force
e.g., “Hooke’s law”
Constitutive physical dependency
Flo
w
Force
Dis
pla
cem
en
t
Force
Mo
me
ntu
m
Flow
Physical dependency class hierarchy
OPB
A Resistive dependency subclass for each physical domain
OPB
Physical dependency by domain
: Paorta PSysVein FSysArt Rartcap:::
FSysArt
=….:
: Paorta PSysVein FSysArt Rartcap:::
FSysArt
=….:
OPB-SemSim working example
SemSim modelSemSim model
Physicalentity
has_property
Physical dependency
Physicalproperty
has_player
Physicalmodel
Computational model
Computation
Data structure
use / return
model code
Neal, M. L., J. H. Gennari, T. Arts, D. L. Cook (2009)Advances in semantic representation of multiscale biosimulations: A case study in merging modelsPac Symp Biocomput (in press)
Conclusion
CellML
SBML
JSim
MatLab
other
SemSimSemSimSemSimSemSim
SemSimSemSimSemSimSemSim
SemSimSemSim
OPB FMA GO
ChEBI etc.
Acknowledgements
SemSim / OPB team• Maxwell L. Neal (Grad student)• Michal Galdzicki (Grad student)• John H. Gennari, PhD (Assoc Prof)• Daniel L. Cook, MD, PhD (Res
Prof)
Bioinformatics• Cornelius Rosse• Onard Mejino• James Brinkley• Todd Detwiler
Partial funding from NIH MLN, MG: T15 LM007442-06 DLC, JHG: R01HL087706-01
UW contributors
Biophysics / biosimulation• James B. Bassingthwaighte• Herbert Sauro• Erik Butterworth• Hong Qian• Adriana Emmi• Fred Bookstein
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
: Paorta PSysVein FSysArt Rartcap::
FSysArt
=….
:
structuralknowledge
physicsknowledge
fluidssolids
chemical kinelectrochem
diffusionheat transfer
Next steps…
SemSim modelSemSim model
Physicalentity
has_property
Physical dependency
Physicalproperty
has_player
Physicalmodel
Computational model
Computation
Data structure
use / return
biosimulation codeGO
ChEBI
FMA
SemGenparse code
access classeswrite new code
Ontology of
Physics for
Biology
VSMVSM
JSim
BAROBARO
JSim
CVCV
JSim
VSMVSM
SemSim
BAROBARO
SemSim
CVCV
SemSim
CV+CV+
SemSim
CV+CV+
JSim
SemSim use-case 1: reuse legacy models
Gennari, J. H., M. L. Neal, B. E, Carlson, D. L. Cook (2008) Integration of multi-scale biosimulation models via light-weight semanticsPac Symp Biocomput (414-425)
3. encode merged SemSimas JSim model
2. use Prompt plug-in to Protégé to analyze and merge SemSim models
1. create SemSim models of JSim
biosimulation models
CVCV
JSim
VSMVSM
SemSim
BAROBARO
SemSim
VSMVSM
JSim
BAROBARO
JSim
CV+CV+
SemSim
CV+CV+
JSim
CircAdaptCircAdapt
MATLAB
CAsys artCA
sys art
JSim
CVCV
SemSim CV-CAsys art
CV-CAsys art
JSim
Neal, M. L., J. H. Gennari, T. Arts, D. L. Cook (2009)Advances in semantic representation of multiscale biosimulations: A case study in merging modelsPac Symp Biocomput (in press)
CAsys artCA
sys art
SemSim
CV-CACV-CA
SemSim
CAsys art
SemSim use-case 2: reuse merged model
1. reuse archived SemSim model
2. create and merge model in different
language
FOLFOL
SemSim
METHMETH
SemSim
FOMCFOMC
SemSim
Fol-allFol-all
SemSim
Fol-allFol-all
JSim
SemSim use-case 3: folate chemical kinetics
Mike Galdzicki, J. H. Gennari, M. L. Neal, D. L. Cook (work in progress)
1. create SemSim models of folate metabolism from published descriptions
FOL
METH
FOMC
2. merge FOL & METH SemSim models
Nijhout, et al. (2004)
Reed, et al. (2004)
Reed, et al. (2006)
FOMCFOMC
JSim
3. encode SemSim models in JSim;
compare model output
Model are complex but can be parsed
Yngve, G., J. F. Brinkley, D. L. Cook, L. G. Shapiro (2007) A Model Browser for BiosimulationAMIA Annu Symp Proc (836-40)
real Paorta(t) mmHg; // Pressure of aortareal PSysVein(t) mmHg; // Pressure of systemic veinreal FSysArt(t) ml/sec; // Flow in systemic artery
real Rartcap = 0.7 mmHg*sec/ml; // Arterial resistance
FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's
Law
real Paorta(t) mmHg; // Pressure of aortareal PSysVein(t) mmHg; // Pressure of systemic veinreal FSysArt(t) ml/sec; // Flow in systemic artery
real Rartcap = 0.7 mmHg*sec/ml; // Arterial resistance
FSysArt = (Paorta - PSysVein) / Rartcap; // Ohm's
Law
physical variables
physical participants
variable dependencies
Ontological maps can be queried for facts
Aortic blood pressure depends on vagus nerve firing rate.
aortic blood pressure
vagus nerve firing rate